{
  "abstract": "Introduction The low coverage of human papillomavirus (HPV) vaccination hinders the elimination of cervical cancer in China. This study aimed to investigate barriers to HPV vaccination using social media data to promote vaccine uptake.Methods Social media posts related to HPV vaccination were analysed on Weibo, China’s leading social media platform from 1 May 2018 to 20 April 2024. Machine learning models, including sentence transformer fine-tuning and bidirectional encoder representations from transformers, were employed for topic modelling.Results From 4 342 736 collected Weibo posts, 817 788 (18.8%) from 610 472 unique users identified barriers to HPV vaccination in mainland China. Key perceived barriers included concerns over adverse effects such as arm pain following vaccination (107 493; 13.1%), adverse effects such as cold and delayed menstruation (77 245; 9.4%) and perceived HPV screening requirement for vaccination and limited vaccine benefits (40 880; 5.0%). Practical barriers predominantly addressed the lack of supply (228 606; 28.0%), age restrictions (135 626; 16.6%) and long waiting time (76 627; 9.4%) for 9-valent vaccine. Temporal analysis revealed fluctuations in barrier prevalence.Conclusions Artificial intelligence methodologies are feasible and valuable in analysing large-scale, publicly available social media data to improve public health. Our results highlight the importance of addressing both perceived and practical barriers to HPV vaccination and provide actionable insights on targeted strategies for reducing HPV-related diseases.",
  "authors": [
    {
      "affiliations": [
        "School of Public Health, National Health Commission Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China"
      ],
      "name": "Zhijun Ding"
    },
    {
      "affiliations": [
        "School of Public Health, National Health Commission Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China"
      ],
      "name": "Haoyun Yang"
    },
    {
      "affiliations": [
        "School of Public Health, National Health Commission Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China",
        "Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Fudan University, Shanghai, China"
      ],
      "name": "Yingchen Zhou"
    },
    {
      "affiliations": [
        "Department of Computer Science, Yale University, New Haven, Connecticut, USA"
      ],
      "name": "Chiyu Wang"
    },
    {
      "affiliations": [
        "School of Public Health, National Health Commission Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China"
      ],
      "name": "Liyan Ma"
    },
    {
      "affiliations": [
        "Shanghai Medical College, Fudan University, Shanghai, China"
      ],
      "name": "Kana Ren"
    },
    {
      "affiliations": [
        "School of Public Health, National Health Commission Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China"
      ],
      "name": "Zhiyuan Hou"
    }
  ],
  "title": "Identifying barriers to human papillomavirus vaccination in China: a topic modelling analysis of social media (2018–2024)",
  "uid": "67689f88-1611-5a1d-abb6-a541a30f77bd"
}